上周估值因子表现较好，本年中证 2000 指数增强策略超额收益为 23.32% ——权益因子观察周报第 125 期 本报告导读： 大类因子表现上，上周沪深 300 内超额收益较好的是估值、市值、价量。中证 500 内较好的是估值、分析师、高频分钟。中证 1000 内较好的是估值、市值、高频分钟。 中证 2000 内较好的是估值、高频分钟、公司治理。中证全指内较好的是估值、高频 分钟、价量。沪深 300 增强上周超额 0.6%；本年超额 5.48%。中证 500 增强上周超 额 0.5%；本年超额 1.35%。中证 1000 增强上周超额 1.8%；本年超额 12.56%。中 证 2000 增强上周超额 1.39%；本年超额 23.32%。 投资要点： 风险提示：量化模型基于历史数据构建，而历史规律存在失效风险。 金融工程 /[Table_Date] 2025.10.14 | [Table_Authors] | 郑雅斌(分析师) | | --- | --- | | | 021-23219395 | | | zhengyabin@gtht.com | | 登记编号 | S0880525040105 | | | 张 ...

以下文章来源于公募排排网 ，作者观在看 公募排排网 . 看财经、查排名、买基金，就上公募排排网，申购费低至0.001折。 本文首发于公众号"公募排排网"。（点击↑↑上图查看详情） 导语 公募排排网 数据显示，全市场 755只公募指数增强基金中，有593只跑赢了对应指数，2025前三季度超额收益均值为3.03%。 分指数来看，中 证2000指数作为量化指增超额的沃土，31只中证2000指增产品前三季度收益收益高达40.74%，超额收益均值高达11.11%，表现最为亮眼。 （ 点此查看公募量化指增前三季度收益概览 ） | 指数名称 | | | 前三季度涨跌(%) 公募指增产品数 今年来收益均值(%) 今年来超额均值(%) | | | --- | --- | --- | --- | --- | | 沪深300指数 | 19.94 | 158 | 19.17 | 1.77 | | 中证500指数 | 29.46 | 156 | 29.6 | 1.48 | | 中证1000指数 | 27.15 | 88 | 34.28 | 8.32 | | 中证2000指数 | 31.74 | 31 | 40.74 | 11.11 | ...

Core Viewpoint - The quantitative private equity industry has rapidly developed in recent years, outperforming public quantitative funds in terms of performance, with private equity quantitative index enhancement products showing an average return of 31.11% compared to 22.03% for public funds [2][3]. Summary by Category Performance Comparison - As of August 15, 2025, the average return for 398 private equity index enhancement products is 31.11%, with an excess return of 11.50%. In contrast, 382 public equity index enhancement products have an average return of 22.03% and an excess return of 6.04% [2][3]. - The performance of private equity products across different indices shows significant advantages, particularly in the 中证500 and 中证1000 categories, where private equity products have average returns of 29.40% and 35.25%, respectively [9][12]. Leading Products - In the 沪深300 index enhancement category, the top private equity product is "澎湃权益1号" managed by 刘治平, achieving an excess return of ***% [5][7]. - For the 中证500 index enhancement, "兆信中证500指数增强1号A类份额" managed by 唐越 and 胡晨航 leads with an excess return of ***% [10][11]. - The top product in the 中证1000 index enhancement is "今通量化价值成长六号" managed by 钱伟强, with an excess return of ***% [13][15]. - In the 国证/中证2000 index enhancement, "平方和鼎盛中证2000指数增强21号A期" managed by 吕杰勇 and 方壮 ranks first with an excess return of ***% [17][19]. Market Environment - The strong performance of quantitative strategies is attributed to the structural characteristics of the A-share market in the first half of 2025, where small and mid-cap stocks have continued to outperform, and individual stock volatility has increased, creating an ideal trading environment for quantitative strategies [3].

Quantitative Models and Construction Methods - **Model Name**: Maximized Factor Exposure (MFE) Portfolio **Model Construction Idea**: The MFE portfolio aims to maximize the exposure of a single factor while controlling for constraints such as industry exposure, style exposure, stock weight deviation, and turnover rate. This approach evaluates the effectiveness of factors under realistic constraints in enhanced index portfolios [56][57][59] **Model Construction Process**: The optimization model is formulated as follows: $ \begin{array}{ll} max & f^{T}w \\ s.t. & s_{l}\leq X(w-w_{b})\leq s_{h} \\ & h_{l}\leq H(w-w_{b})\leq h_{h} \\ & w_{l}\leq w-w_{b}\leq w_{h} \\ & b_{l}\leq B_{b}w\leq b_{h} \\ & 0\leq w\leq l \\ & 1^{T}w=1 \\ & \Sigma|w-w_{0}|\leq to_{h} \end{array} $ - **Objective Function**: Maximize single-factor exposure, where \( f \) represents factor values, and \( w \) is the stock weight vector - **Constraints**: 1. Style exposure deviation (\( X \)): \( s_{l} \) and \( s_{h} \) are the lower and upper bounds for style factor deviation 2. Industry exposure deviation (\( H \)): \( h_{l} \) and \( h_{h} \) are the lower and upper bounds for industry deviation 3. Stock weight deviation (\( w_{l} \) and \( w_{h} \)): Limits on individual stock weight deviation relative to the benchmark 4. Component weight limits (\( b_{l} \) and \( b_{h} \)): Constraints on the weight of benchmark components 5. No short selling and upper limits on stock weights 6. Full investment constraint: \( 1^{T}w=1 \) 7. Turnover constraint: \( \Sigma|w-w_{0}|\leq to_{h} \), where \( w_{0} \) is the previous period's weight [56][57][59] **Model Evaluation**: The model effectively balances factor exposure and practical constraints, ensuring stable returns and avoiding excessive concentration in specific stocks [60] --- Quantitative Factors and Construction Methods - **Factor Name**: Six-Month UMR **Factor Construction Idea**: The six-month UMR factor measures risk-adjusted momentum over a six-month window, capturing medium-term momentum trends [19][8][44] **Factor Construction Process**: - The UMR (Up-Minus-Down Ratio) is calculated as the ratio of upward movements to downward movements in stock prices over a specified period - The six-month UMR specifically uses a six-month window to compute this ratio, adjusted for risk [19][8][44] **Factor Evaluation**: This factor demonstrates strong performance in various index spaces, particularly in the CSI 500 and CSI All Share indices, indicating its effectiveness in capturing medium-term momentum [8][44] - **Factor Name**: Three-Month UMR **Factor Construction Idea**: Similar to the six-month UMR, this factor focuses on shorter-term momentum trends over a three-month window [19][8][44] **Factor Construction Process**: - The three-month UMR is calculated using the same methodology as the six-month UMR but with a three-month window for data aggregation [19][8][44] **Factor Evaluation**: This factor shows consistent performance across multiple indices, including the CSI 500 and CSI All Share indices, making it a reliable short-term momentum indicator [8][44] - **Factor Name**: Pre-Tax Earnings to Total Market Value (EPTTM) **Factor Construction Idea**: This valuation factor evaluates the earnings yield of a stock, providing insights into its relative valuation [19][8][44] **Factor Construction Process**: - EPTTM is calculated as the ratio of pre-tax earnings to the total market value of a stock, with adjustments for rolling time windows (e.g., one year) [19][8][44] **Factor Evaluation**: EPTTM consistently ranks among the top-performing valuation factors, particularly in the CSI 300 and CSI 800 indices, reflecting its robustness in identifying undervalued stocks [8][44] --- Backtesting Results of Models - **MFE Portfolio**: - The MFE portfolio demonstrates strong performance under various constraints, with backtesting results showing significant alpha generation relative to benchmarks like CSI 300, CSI 500, and CSI 1000 [60][61] --- Backtesting Results of Factors - **Six-Month UMR**: - CSI 500: Weekly return of 0.99%, monthly return of 1.65%, annualized return of -4.07% [26] - CSI All Share: Weekly return of 1.23%, monthly return of 1.59%, annualized return of 7.43% [44] - **Three-Month UMR**: - CSI 500: Weekly return of 0.94%, monthly return of 1.31%, annualized return of 0.68% [26] - CSI All Share: Weekly return of 1.02%, monthly return of 1.63%, annualized return of 5.64% [44] - **EPTTM**: - CSI 300: Weekly return of 0.74%, monthly return of 1.42%, annualized return of 3.89% [22] - CSI 800: Weekly return of 1.00%, monthly return of 1.91%, annualized return of 2.87% [30]